{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HEK293 identification statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "src_dir = os.path.abspath('../src')\n",
    "if src_dir not in sys.path:\n",
    "    sys.path.append(src_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import collections\n",
    "import itertools\n",
    "import string\n",
    "\n",
    "import matplotlib.patheffects as path_effects\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mticker\n",
    "import pandas as pd\n",
    "import pyteomics.auxiliary\n",
    "import seaborn as sns\n",
    "import tqdm\n",
    "from matplotlib_venn import venn3, venn3_circles\n",
    "\n",
    "from ann_solo import reader, spectrum, util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# plot styling\n",
    "plt.style.use(['seaborn-white', 'seaborn-paper'])\n",
    "plt.rc('font', family='serif')\n",
    "sns.set_palette('Set1')\n",
    "sns.set_context('paper', font_scale=1.3)    # single-column figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tqdm.tqdm = tqdm.tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_time_from_log(filename):\n",
    "    with open(filename, 'r') as f_in:\n",
    "        for line in f_in:\n",
    "            if line.startswith('user'):\n",
    "                # user time\n",
    "                usertime = line.split()[1]\n",
    "                minutes = int(usertime[:usertime.find('m')])\n",
    "                seconds = float(usertime[usertime.find('m') + 1: usertime.rfind('s')])\n",
    "                usertime = minutes * 60 + seconds\n",
    "                # sys time\n",
    "                line = next(f_in)\n",
    "                systime = line.split()[1]\n",
    "                minutes = int(systime[:systime.find('m')])\n",
    "                seconds = float(systime[systime.find('m') + 1: systime.rfind('s')])\n",
    "                systime = minutes * 60 + seconds\n",
    "                \n",
    "                return usertime + systime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_spectrast_psms(filename):\n",
    "    psms = []\n",
    "    psms_df = pd.read_csv(filename, sep='\\t', header=0)\n",
    "    for psm_tuple in psms_df.itertuples(False):\n",
    "        sequence, charge = psm_tuple.ID.split('/')\n",
    "        query_spectrum = spectrum.Spectrum(psm_tuple._0, psm_tuple.MzDiff,\n",
    "                                           int(charge))\n",
    "        psm = spectrum.SpectrumMatch(query_spectrum,\n",
    "                                     search_engine_score=psm_tuple.Dot)\n",
    "        psm.sequence = sequence\n",
    "        psm.calc_mass_to_charge = 0\n",
    "        psm.is_decoy = 'DECOY_' in psm_tuple.Proteins\n",
    "        psms.append(psm)\n",
    "    return psms\n",
    "\n",
    "def read_msfragger_psms(filename):\n",
    "    psms = []\n",
    "    psms_df = pd.read_csv(\n",
    "        filename, sep='\\t', header=None, names=[\n",
    "            'ScanID', 'Precursor neutral mass (Da)',\n",
    "            'Retention time (minutes)', 'Precursor charge', 'Hit rank',\n",
    "            'Peptide Sequence', 'Upstream Amino Acid', 'Downstream Amino Acid',\n",
    "            'Protein', 'Matched fragment ions',\n",
    "            'Total possible number of matched theoretical fragment ions',\n",
    "            'Neutral mass of peptide (including any variable modifications) (Da)',\n",
    "            'Mass difference', 'Number of tryptic termini',\n",
    "            'Number of missed cleavages', 'Variable modifications detected',\n",
    "            'Hyperscore', 'Next score', 'Intercept of expectation model',\n",
    "            'Slope of expectation model'])\n",
    "    for psm_tuple in psms_df.itertuples(False):\n",
    "        charge = psm_tuple._3\n",
    "        mz = psm_tuple._12 / charge\n",
    "        query_spectrum = spectrum.Spectrum(psm_tuple.ScanID, mz, charge, psm_tuple._2)\n",
    "        psm = spectrum.SpectrumMatch(query_spectrum,\n",
    "                                     search_engine_score=psm_tuple.Hyperscore)\n",
    "        psm.sequence = psm_tuple._5\n",
    "        psm.calc_mass_to_charge = 0\n",
    "        psm.is_decoy = 'decoy_' in psm_tuple.Protein\n",
    "        psms.append(psm)\n",
    "    return psms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def psms_to_df(psms):\n",
    "    sequences, ids, scores, charges, exp_masses, calc_masses, decoys = ([] for _ in range(7))\n",
    "    for psm in psms:\n",
    "        sequences.append(psm.sequence)\n",
    "        ids.append(psm.query_id)\n",
    "        scores.append(psm.search_engine_score)\n",
    "        charges.append(psm.charge)\n",
    "        exp_masses.append(psm.exp_mass_to_charge)\n",
    "        calc_masses.append(psm.calc_mass_to_charge)\n",
    "        decoys.append(psm.is_decoy)\n",
    "    \n",
    "    return pd.DataFrame({'sequence': sequences, 'PSM_ID': ids,\n",
    "                         'search_engine_score[1]': scores, 'charge': charges,\n",
    "                         'exp_mass_to_charge': exp_masses,\n",
    "                         'calc_mass_to_charge': calc_masses,\n",
    "                         'opt_ms_run[1]_cv_MS:1002217_decoy_peptide': decoys})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "max_fdr = 0.01\n",
    "tol_mass = 0.1\n",
    "tol_mode = 'Da'\n",
    "min_group_size = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hek293_dir = '../data/processed/hek293'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "fdf58262bb3040eb94d3049a96f4758c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Files processed', max=288), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Wout/.conda/envs/ann-solo/lib/python3.6/site-packages/pyteomics/auxiliary/target_decoy.py:79: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  q = tfalse / (ind - cumsum) / ratio\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "num_ids = []\n",
    "runtimes = []\n",
    "psms = collections.defaultdict(list)\n",
    "total = 24 * 2 * 6    # 24 raw files * 2 (IDs & log) * 6 (search engine combos)\n",
    "with tqdm.tqdm(desc='Files processed', unit='files', total=total) as pbar:\n",
    "    for search_engine in ('ann-solo', 'spectrast', 'msfragger'):\n",
    "        for search_mode in ('oms', 'std'):\n",
    "            for filename in os.listdir(\n",
    "                    os.path.join(hek293_dir, search_engine, search_mode)):\n",
    "                filename_full = os.path.join(hek293_dir, search_engine,\n",
    "                                             search_mode, filename)\n",
    "                _, ext = os.path.splitext(filename_full)\n",
    "                if ext == '.log':\n",
    "                    runtimes.append((\n",
    "                        search_engine, search_mode,\n",
    "                        os.path.splitext(filename)[0],\n",
    "                        extract_time_from_log(filename_full)))\n",
    "                    pbar.update(1)\n",
    "                elif ext in ('.mztab', '.txt', '.tsv'):\n",
    "                    if ext == '.mztab':  # ANN-SoLo\n",
    "                        file_psms = reader.read_mztab_psms(filename_full)\n",
    "                    elif ext == '.txt':  # SpectraST\n",
    "                        file_psms = psms_to_df(list(util.filter_group_fdr(\n",
    "                            read_spectrast_psms(filename_full),\n",
    "                            max_fdr, tol_mass, tol_mode, min_group_size)))\n",
    "                    elif ext == '.tsv':  # MSFragger\n",
    "                        file_psms = psms_to_df(list(util.filter_group_fdr(\n",
    "                            read_msfragger_psms(filename_full),\n",
    "                            max_fdr, tol_mass, tol_mode, min_group_size)))\n",
    "                    psms[(search_engine, search_mode)].append(file_psms)\n",
    "                    num_ids.append((search_engine, search_mode,\n",
    "                                    os.path.splitext(filename)[0],\n",
    "                                    len(file_psms)))\n",
    "                    pbar.update(1)\n",
    "\n",
    "num_ids_df = pd.DataFrame.from_records(\n",
    "    num_ids, columns=['search_engine', 'search_mode', 'filename', 'psms'])\n",
    "time_df = pd.DataFrame.from_records(\n",
    "    runtimes, columns=['search_engine', 'search_mode', 'filename', 'time'])\n",
    "stats = (pd.merge(num_ids_df, time_df,\n",
    "                 on=['search_engine', 'search_mode', 'filename'])\n",
    "         .sort_values(['search_engine', 'search_mode', 'filename'])\n",
    "         .reset_index(drop=True))\n",
    "\n",
    "summary = (stats.groupby(['search_engine', 'search_mode'])\n",
    "           .agg({'psms': 'sum', 'time': 'mean'}))\n",
    "summary['time'] = summary['time'] / 60\n",
    "\n",
    "psms_df, peptides = [], collections.defaultdict(dict)\n",
    "for (search_engine, search_mode), psm_list in psms.items():\n",
    "    sequences = set(pd.concat(psm_list)['sequence']\n",
    "                   .str.replace(r'n?\\[\\d+\\]', '').unique())\n",
    "    psms_df.append((search_engine, search_mode, len(sequences)))\n",
    "    peptides[search_mode][search_engine] = sequences\n",
    "psms_df = pd.DataFrame.from_records(\n",
    "    psms_df, index=['search_engine', 'search_mode'],\n",
    "    columns=['search_engine', 'search_mode', 'peptides'])\n",
    "summary = summary.join(psms_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>psms</th>\n",
       "      <th>time</th>\n",
       "      <th>peptides</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search_engine</th>\n",
       "      <th>search_mode</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">ann-solo</th>\n",
       "      <th>oms</th>\n",
       "      <td>647469</td>\n",
       "      <td>108.531303</td>\n",
       "      <td>153605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>352938</td>\n",
       "      <td>24.015849</td>\n",
       "      <td>105870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">msfragger</th>\n",
       "      <th>oms</th>\n",
       "      <td>526027</td>\n",
       "      <td>34.745542</td>\n",
       "      <td>126364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>344998</td>\n",
       "      <td>0.697961</td>\n",
       "      <td>104672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">spectrast</th>\n",
       "      <th>oms</th>\n",
       "      <td>473729</td>\n",
       "      <td>1276.663253</td>\n",
       "      <td>112375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>369079</td>\n",
       "      <td>5.205294</td>\n",
       "      <td>102077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             psms         time  peptides\n",
       "search_engine search_mode                               \n",
       "ann-solo      oms          647469   108.531303    153605\n",
       "              std          352938    24.015849    105870\n",
       "msfragger     oms          526027    34.745542    126364\n",
       "              std          344998     0.697961    104672\n",
       "spectrast     oms          473729  1276.663253    112375\n",
       "              std          369079     5.205294    102077"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# export LaTeX table\n",
    "with open('../../reports/rev/tab-hek293_stats.tex', 'w') as f_out:\n",
    "    f_out.write('\\\\begin{tabularx}{0.5\\\\textwidth}{Xrrr}\\n')\n",
    "    f_out.write('\\\\toprule\\n')\n",
    "    f_out.write('Search engine & Time (\\\\si{\\\\minute}) & \\\\#~SSMs & \\\\#~Peptides \\\\\\\\\\n')\n",
    "    f_out.write('\\\\midrule\\n')\n",
    "    f_out.write('\\\\multicolumn{4}{l}{\\\\emph{Standard search}} \\\\\\\\\\n')\n",
    "    for search_engine in ('MSFragger', 'SpectraST', 'ANN-SoLo'):\n",
    "        num_psms, time, num_pep = summary.loc[search_engine.lower(), 'std']\n",
    "        f_out.write(f'\\\\mbox{{{search_engine}}} & \\\\num{{{round(time, 1)}}} & '\n",
    "                    f'\\\\num{{{int(num_psms)}}} & \\\\num{{{int(num_pep)}}} \\\\\\\\\\n')\n",
    "    f_out.write('\\\\addlinespace\\n')\n",
    "    f_out.write('\\\\multicolumn{4}{l}{\\\\emph{Open search}} \\\\\\\\\\n')\n",
    "    for search_engine in ('MSFragger', 'SpectraST', 'ANN-SoLo'):\n",
    "        num_psms, time, num_pep = summary.loc[search_engine.lower(), 'oms']\n",
    "        f_out.write(f'\\\\mbox{{{search_engine}}} & \\\\num{{{round(time, 1)}}} & '\n",
    "                    f'\\\\num{{{int(num_psms)}}} & \\\\num{{{int(num_pep)}}} \\\\\\\\\\n')\n",
    "    f_out.write('\\\\bottomrule\\n')\n",
    "    f_out.write('\\\\end{tabularx}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for search_mode, search_engines in peptides.items():\n",
    "    for search_engine, sequences in search_engines.items():\n",
    "        peptides[search_mode][search_engine] = set([pep.replace('I', 'L') for pep in sequences])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3oPVS8pbjcrHVmsf3Gi1bcnIoHRhMvfJR1C1ThbqhVQkwFu+majannT2XTvLD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\nTd43IWRwOH3+MkLvnWuUcm0AkLf9V/hP9YNQLATDYWiwTgAA9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TK5fvcnhJD+Y1kW+44l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\n5c852bODPm0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x151614c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height = 7\n",
    "width = height * 2\n",
    "fig, axes = plt.subplots(1, 2, figsize=(width, height))\n",
    "\n",
    "label_map = {'ann-solo': 'ANN-SoLo', 'msfragger': 'MSFragger',\n",
    "             'spectrast': 'SpectraST'}\n",
    "for i, (search_mode, ax) in enumerate(zip(('std', 'oms'), axes)):\n",
    "    v = venn3(peptides[search_mode].values(),\n",
    "              [label_map[key] for key in peptides[search_mode].keys()],\n",
    "              set_colors=[next(ax._get_lines.prop_cycler)['color'],\n",
    "                          next(ax._get_lines.prop_cycler)['color'],\n",
    "                          next(ax._get_lines.prop_cycler)['color']],\n",
    "              alpha=1, ax=ax)\n",
    "    # make sure the numbers are legible\n",
    "    for text in v.subset_labels:\n",
    "        text.set_color('white')\n",
    "        text.set_path_effects([path_effects.Stroke(linewidth=3, foreground='black'),\n",
    "                               path_effects.Normal()])\n",
    "    c = venn3_circles(peptides[search_mode].values(), linewidth=1.0, ax=ax)\n",
    "    \n",
    "    ax.annotate(string.ascii_uppercase[i], xy=(0.5 * i, 1), xytext=(5, -20),\n",
    "                xycoords='figure fraction', textcoords='offset pixels',\n",
    "                fontsize='xx-large', weight='bold')\n",
    "    \n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('hek293_peptides_venn.pdf', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_mass_groups(psms, tol_mass, tol_mode):\n",
    "    # start with the highest ranked PSM\n",
    "    mass_groups = []\n",
    "    psms_remaining = psms.sort_values('search_engine_score[1]', ascending=False)\n",
    "    while len(psms_remaining) > 0:\n",
    "        # find all remaining PSMs within the precursor mass window\n",
    "        mass_diff = psms_remaining['mass_diff'].iloc[0]\n",
    "        if tol_mass is None or tol_mode not in ('Da', 'ppm'):\n",
    "            psms_selected = psms_remaining\n",
    "        elif tol_mode == 'Da':\n",
    "            psms_selected = \\\n",
    "                psms_remaining[(psms_remaining['mass_diff'] - mass_diff).abs()\n",
    "                               <= tol_mass]\n",
    "        elif tol_mode == 'ppm':\n",
    "            psms_selected = \\\n",
    "                psms_remaining[(psms_remaining['mass_diff'] - mass_diff).abs()\n",
    "                               / psms_remaining['exp_mass_to_charge'] * 10 ** 6\n",
    "                               <= tol_mass]\n",
    "        mass_groups.append(psms_selected)\n",
    "        # exclude the selected PSMs from further selections\n",
    "        psms_remaining.drop(psms_selected.index, inplace=True)\n",
    "\n",
    "    mass_group_stats = []\n",
    "    for mass_group in mass_groups:\n",
    "        mass_group_stats.append((mass_group['mass_diff'].median(),\n",
    "                                 mass_group['mass_diff'].mean(),\n",
    "                                 len(mass_group)))\n",
    "    mass_group_stats = pd.DataFrame.from_records(\n",
    "        mass_group_stats, columns=['mass_diff_median', 'mass_diff_mean', 'num_psms'])\n",
    "    return mass_group_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for psms_key, psms_list in psms.items():\n",
    "    psms[psms_key] = pd.concat(psms_list, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "search_engines = ('ANN-SoLo', 'SpectraST', 'MSFragger')\n",
    "for search_engine in search_engines:\n",
    "    psms_oms = psms[(search_engine.lower(), 'oms')]\n",
    "    psms_oms['mass_diff'] = (\n",
    "        (psms_oms['exp_mass_to_charge'] - psms_oms['calc_mass_to_charge'])\n",
    "        * psms_oms['charge'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mass_groups = {}\n",
    "for search_engine in search_engines:\n",
    "    psms_oms = psms[(search_engine.lower(), 'oms')]\n",
    "    psms_oms['mass_diff'] = (\n",
    "        (psms_oms['exp_mass_to_charge'] - psms_oms['calc_mass_to_charge'])\n",
    "        * psms_oms['charge'])\n",
    "    mass_groups[search_engine.lower()] = get_mass_groups(psms_oms, tol_mass, tol_mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sNFpf/RwvLCxM1MCILJ21PcMiIuthNjlPmTIFw4YNQ/fu3Q3WC4KAuLg40QNraVr7f+St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\nYRiG0TGscWYYhmEYHfN/Sf6LTsg7oMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1479b75f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 7\n",
    "height = width / 1.618 / 2 * len(search_engines)\n",
    "fig, axes = plt.subplots(len(search_engines), 1, sharex=True, figsize=(width, height))\n",
    "\n",
    "for ax, search_engine in zip(axes, search_engines):\n",
    "    mass_group = mass_groups[search_engine.lower()]\n",
    "    mask = mass_group['mass_diff_median'].abs() > tol_mass   # exclude unmodified PSMs\n",
    "    ax.bar(mass_group[mask]['mass_diff_median'], mass_group[mask]['num_psms'],\n",
    "           width=0.4, log=False, color='black')\n",
    "\n",
    "    ax.set_xlim((-50, 200))\n",
    "\n",
    "    # format y-axis numbers\n",
    "    ax.yaxis.set_major_formatter(mticker.StrMethodFormatter('{x:,g}'))\n",
    "    \n",
    "    ax.set_ylabel(search_engine)\n",
    "\n",
    "    sns.despine(ax=ax)\n",
    "    \n",
    "# set tick labels at nice positions\n",
    "axes[-1].xaxis.set_major_locator(mticker.MultipleLocator(50))\n",
    "axes[-1].set_xlabel('Precursor mass difference (Da)')\n",
    "\n",
    "ax = fig.add_subplot(111, frameon=False)\n",
    "ax.grid(False)\n",
    "ax.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')\n",
    "ax.set_ylabel(f'Number of SSMs (FDR={max_fdr})', labelpad=40)\n",
    "\n",
    "plt.savefig('mass_diff.pdf', dpi=300, bbox_inches='tight')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
